Impressions in Recommender Systems: Present and Future

Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems

Characterizing Impression-Aware Recommender Systems

Impression-aware recommender systems (IARS) are a type of recommenders that learn user preferences using their interactions and the recommendations (also known as impressions) shown to users. The community’s interest in this type of recommenders has steadily increased in recent years. To aid in characterizing this type of recommenders, we propose a theoretical framework to define IARS and classify the recommenders present in the state-of-the-art. We start this work by defining core concepts related to this type of recommenders, such as impressions and user feedback. Based on this theoretical framework, we identify and define three properties and three taxonomies that characterize IARS. Lastly, we undergo a systematic literature review where we discover and select papers belonging to the state-of-the-art. Our review analyzes papers under the properties and taxonomies we propose; we highlight the most and least common properties and taxonomies used in the literature, their relations, and their evolution over time, among others

Incorporating Impressions to Graph-Based Recommenders

Graph-based approaches have become an effective strategy to model the users’ preferences in recommender systems accurately; however, despite their excellent recommendation quality, the literature still needs to incorporate impressions (past recommendations) into existing approaches. By their definition, impressions contain the selection of the most relevant items for the user; enriching the users’ profiles with those items may lead to higher-quality recommendations. In this work, we propose and empirically explore the effectiveness of two approaches that include impressions into graph-based recommenders. Both approaches are simple yet extensible as they do not change the definitions of the recommenders; but transform their main data structure: the graph’s adjacency matrix. The results of our experiments suggest that our approaches may improve the recommendation quality of graph-based recommenders that do not use impressions; however, we also find that beyond-accuracy metrics may become negatively affected

Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain

Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain

This paper presents the solution designed by the team "Boston Team Party"for the ACM RecSys Challenge 2022. The competition was organized by Dressipi and was framed under the session-based fashion recommendations domain. Particularly, the task was to predict the purchased item at the end of each anonymous session. Our proposed two-stage solution is effective, lightweight, and scalable. First, it leverages the expertise of several strong recommendation models to produce a pool of candidate items. Then, a Gradient-Boosting Decision Tree model aggregates these candidates alongside several hand-crafted features to produce the final ranking. Our model achieved a score of 0.18800 in the public leaderboard. To aid in the reproducibility of our findings, we open-source our materials

Visible PL phenomenon at room temperature in disordered structure of SrWO 4 powder

Abstract. The SrWO 4 (SWO) powders were synthesized by the polymeric precursor method and annealed at different temperatures. The SWO structure was obtained by X-ray diffraction and the corresponding photoluminescence (PL) spectra was measured. The PL results reveal that the structural order-disorder degree in the SWO lattice influences in the PL emission intensity. Only the structurally order-disordered samples present broad and intense PL band in the visible range. To understand the origin of this phenomenon, we performed quantum-mechanical calculations with crystalline and order-disordered SWO periodic models. Their electronic structures were analyzed in terms of band structure. The appearance of localized levels in the band gap of the order-disordered structure was evidenced and is a favorable condition for the intense PL to occur

Measuring the ranking quality of recommendations in a two-dimensional carousel setting

Movie-on-demand and music streaming services usually provide the user with multiple recommendation lists, i.e., carousels, in a two-dimensional user interface, each generated according to different criteria (e.g., TV series, popular artists, etc.). In this two-dimensional setting it is not appropriate to use traditional ranking metrics designed for a single ranking list. It is well known that users do not explore a two-dimensional interface one row at a time, but rather focus their attention in a triangular area at the top-left corner. Furthermore, it is frequent for user interfaces to hide some items or lists due to space constraints, which can be shown by performing certain actions (i.e., click, swipe). In this paper we extend the widely used NDCG to a two-dimensional recommendation setting with a formulation that allows to account both the two-dimensional user exploration behaviour and interface-specific design. We also compare the proposed extension against single-list NDCG highlighting that they can lead to a different choice of the optimal algorithm in offline evaluation